Training a model in a Jupyter Notebook is satisfying. But deploying it? That’s where the headaches usually start. Today, I’m going to show you how to deploy a Keras image classifier using AWS Lambda and TensorFlow Lite.
<h2>Why Serverless?</h2>
AWS Lambda is “Serverless,” meaning you don’t manage the OS or hardware. You just upload code. It’s cheap because you only pay when your code runs.
The Heavyweight Problem 🏋️
Standard TensorFlow is huge (approx. 1.7 GB). If you try to shove this into a Lambda function, you’ll run into storage issues and slow performance.
<h2>The Lightweight Solution ⚡</h2>
We use TensorFlow Lite. It optimizes the model for inference (prediction) only, stripping out all the training logic.
<h2>Step 1: The Handler Code</h2>
Your Python script needs a special function to handle the AWS event:
<pre><code>import tflite_runtime.interpreter as tflite
from keras_image_helper import create_preprocessor

interpreter = tflite.Interpreter(model_path=&#x27;clothing-model.tflite&#x27;)
interpreter.allocate_tensors()

def lambda_handler(event, context):
 url = event[&#x27;url&#x27;]
 # ... preprocessing and inference logic ...
 return result
</code></pre>
<h2>Step 2: The Dockerfile</h2>
We use Docker to package our dependencies. Crucial Tip: When installing the TF-Lite runtime from a URL, ensure you use the <code>raw</code> version of the link, or <code>pip</code> will throw a <code>BadZipFile</code> error.
<h2>Step 3: Deploy with Serverless Framework</h2>
Instead of clicking buttons in the AWS console, we can use a <code>serverless.yml</code> file to describe our infrastructure:
<pre><code>service: clothing-model
provider:
 name: aws
 ecr:
 images:
 appimage:
 path: ./
functions:
 predict:
 image:
 name: appimage
 events:
 - http:
 path: predict
 method: post
</code></pre>
Running <code>serverless deploy</code> handles the Docker build, ECR upload, and Lambda creation automatically!
Happy coding!

Austin Deyan

Learn to deploy Keras image classifiers using AWS Lambda and TensorFlow Lite to avoid the storage and performance issues of standard TensorFlow. The guide covers creating a Lambda handler function, packaging dependencies with Docker (including a crucial tip about TF-Lite runtime installation), and automating deployment using the Serverless Framework with a serverless.yml configuration file.

How to Deploy Your ML Models for Free (Almost) using AWS Lambda